Jiefeng Chen

According to our database, Jiefeng Chen authored at least 28 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

ASPEST: Bridging the Gap Between Active Learning and Selective Prediction.

Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Ö. Arik, Somesh Jha, Tomas Pfister

Trans. Mach. Learn. Res., 2024

Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models.

Fei Wang, Xingchen Wan, Ruoxi Sun, Jiefeng Chen, Sercan Ö. Arik

CoRR, 2024

Two Heads are Actually Better than One: Towards Better Adversarial Robustness via Transduction and Rejection.

Nils Palumbo, Yang Guo, Xi Wu, Jiefeng Chen, Yingyu Liang, Somesh Jha

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Two Heads are Better than One: Towards Better Adversarial Robustness by Combining Transduction and Rejection.

Nils Palumbo, Yang Guo, Xi Wu, Jiefeng Chen, Yingyu Liang, Somesh Jha

CoRR, 2023

Concept-based Explanations for Out-of-Distribution Detectors.

Jihye Choi, Jayaram Raghuram, Ryan Feng, Jiefeng Chen, Somesh Jha, Atul Prakash

Proceedings of the International Conference on Machine Learning, 2023

Stratified Adversarial Robustness with Rejection.

Jiefeng Chen, Jayaram Raghuram, Jihye Choi, Xi Wu, Yingyu Liang, Somesh Jha

Proceedings of the International Conference on Machine Learning, 2023

The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning.

Zhenmei Shi, Jiefeng Chen, Kunyang Li, Jayaram Raghuram, Xi Wu, Yingyu Liang, Somesh Jha

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Is Forgetting Less a Good Inductive Bias for Forward Transfer?

Jiefeng Chen, Timothy Nguyen, Dilan Görür, Arslan Chaudhry

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs.

Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Ö. Arik, Tomas Pfister, Somesh Jha

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

2022

Toward Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-Based Method.

Tao Bai, Jun Zhao, Jinlin Zhu, Shoudong Han, Jiefeng Chen, Bo Li, Alex ChiChung Kot

IEEE Internet Things J., 2022

Towards Evaluating the Robustness of Neural Networks Learned by Transduction.

Jiefeng Chen, Xi Wu, Yang Guo, Yingyu Liang, Somesh Jha

Proceedings of the Tenth International Conference on Learning Representations, 2022

GRAPHITE: Generating Automatic Physical Examples for Machine-Learning Attacks on Computer Vision Systems.

Ryan Feng, Neal Mangaokar, Jiefeng Chen, Earlence Fernandes, Somesh Jha, Atul Prakash

Proceedings of the 7th IEEE European Symposium on Security and Privacy, 2022

2021

Towards Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-based Method.

Tao Bai, Jun Zhao, Jinlin Zhu, Shoudong Han, Jiefeng Chen, Bo Li, Alex C. Kot

CoRR, 2021

Towards Adversarial Robustness via Transductive Learning.

Jiefeng Chen, Yang Guo, Xi Wu, Tianqi Li, Qicheng Lao, Yingyu Liang, Somesh Jha

CoRR, 2021

ATOM: Robustifying Out-of-Distribution Detection Using Outlier Mining.

Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles.

Jiefeng Chen, Frederick Liu, Besim Avci, Xi Wu, Yingyu Liang, Somesh Jha

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

AI-GAN: Attack-Inspired Generation of Adversarial Examples.

Tao Bai, Jun Zhao, Jinlin Zhu, Shoudong Han, Jiefeng Chen, Bo Li, Alex C. Kot

Proceedings of the 2021 IEEE International Conference on Image Processing, 2021

2020

Robust Out-of-distribution Detection via Informative Outlier Mining.

Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha

CoRR, 2020

Representation Bayesian Risk Decompositions and Multi-Source Domain Adaptation.

Xi Wu, Yang Guo, Jiefeng Chen, Yingyu Liang, Somesh Jha, Prasad Chalasani

CoRR, 2020

Robust Out-of-distribution Detection in Neural Networks.

Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha

CoRR, 2020

Query-Efficient Physical Hard-Label Attacks on Deep Learning Visual Classification.

Ryan Feng, Jiefeng Chen, Nelson R. Manohar, Earlence Fernandes, Somesh Jha, Atul Prakash

CoRR, 2020

AI-GAN: Attack-Inspired Generation of Adversarial Examples.

Tao Bai, Jun Zhao, Jinlin Zhu, Shoudong Han, Jiefeng Chen, Bo Li

CoRR, 2020

Concise Explanations of Neural Networks using Adversarial Training.

Prasad Chalasani, Jiefeng Chen, Amrita Roy Chowdhury, Xi Wu, Somesh Jha

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Robust Attribution Regularization.

Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Towards Understanding Limitations of Pixel Discretization Against Adversarial Attacks.

Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha

Proceedings of the IEEE European Symposium on Security and Privacy, 2019

2018

Improving Adversarial Robustness by Data-Specific Discretization.

Jiefeng Chen, Xi Wu, Yingyu Liang, Somesh Jha

CoRR, 2018

Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training.

Xi Wu, Uyeong Jang, Jiefeng Chen, Lingjiao Chen, Somesh Jha

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

ReabsNet: Detecting and Revising Adversarial Examples.

Jiefeng Chen, Zihang Meng, Changtian Sun, Wei Tang, Yinglun Zhu

CoRR, 2017